Probabilistic Metamodels to Quantify Uncertainties in Electric Powertrain Whining Noise Contribution 2023-01-1071

With electromobility, vehicles are becoming quieter due to the presence of electric motors that replace internal combustion engines. The interior cabin noise of electric vehicles is characterized by high-frequency components that can be annoying and unpleasant. Therefore, it is essential to analyse the NVH behaviour of e-powertrains early in the design-phase. However, this induces inherent uncertainties during the design process related to the operating conditions, geometrical parameters, measurement techniques, etc. that need to be quantified with fast and comprehensive stochastic models.

In this work, we first present a deterministic framework to provide first-order estimations of the e-powertrain’s interior whining noises, combining both the airborne & structure-borne contribution with data-driven NVH transfers meta-models. Subsequently, stochasticity is induced in the deterministic models considering random sampling of operating conditions and the chosen geometrical parameters for the e-machine under assessment. At each stage, metamodels (or surrogates), developed in the Bayesian framework, take into account the uncertainties which are propagated from operating conditions to whining noise contribution inside the cabin. The work is demonstrated on an interior permanent magnet synchronous motor which is widely used in electric vehicles traction drives.